Triple

T24240578
Position Surface form Disambiguated ID Type / Status
Subject The Woman in Question E603211 entity
Predicate producer P490 FINISHED
Object Antony Darnborough
Antony Darnborough was a British film producer active in the mid-20th century, known for his work on several notable postwar British dramas and thrillers.
E1648336 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Antony Darnborough | Statement: [The Woman in Question, producer, Antony Darnborough]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Antony Darnborough
Triple: [The Woman in Question, producer, Antony Darnborough]
Generated description
Antony Darnborough was a British film producer active in the mid-20th century, known for his work on several notable postwar British dramas and thrillers.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e2953f631c819097cbb421046bd417 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28a9f812c81909dba8fbb54d8985c completed April 29, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a100fd3e4008190991997c66a712ae5 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10140b2fec8190aa6d805f54926b56 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 12:03 a.m.